An oral cavity soft and hard tissue CT sequence and three-dimensional grid model registration method

Abstract

The invention provides an oral cavity soft and hard tissue CT sequence and three-dimensional grid model registration method comprising the following steps of: firstly manually dividing a biting impression model to obtain the part without a pedestal in the biting impression model; secondly, according to rough tooth outlines of user editing and calibrating, dividing input CT sequences to obtain the coordinates of the tooth outlines in the CT sequences, and constructing a hard tissue point set with the point coordinates of tooth hard tissue outlines of input layers of CT sequences; thirdly using iterative closest point algorithm for registration of the point set coordinates of the biting impression model and the point set coordinates of the hard tissue outlines to obtain the rough registration position in three-dimensional space of the biting impression model; fourthly, using the rough registration position obtained through the ICP algorithm as the initial result of accurate registration, constructing initial energy function values for the given CT sequences and the position of a three-dimensional grid model of initial registration, and optimizing the energy function through the quasi-Newton method to obtain an accurate registration result. The method enables superposition display of registration of CT sequences and biting impression models and can measure the thickness of soft tissue in CT sequences.

Description

technical field [0001] The invention relates to the fields of image processing, computer vision and augmented reality, in particular to a method for registering a CT sequence of oral soft and hard tissues and a three-dimensional grid model. Background technique [0002] At present, some scientific researchers have carried out research on related technologies. In terms of image segmentation, in 2005, Chun-MingLi of Connecticut proposed an improved level-set method based on the variational method [5], which does not require re-initialization of the initial boundary calibrated by the user. Compared with the traditional level set method, this method has the following advantages: it can use a larger time stamp value to improve the convergence speed of the algorithm and reduce the calculation time; it can use the generalized calibration area boundary as the initial boundary without being affected by other Must be a restriction on the distance function; can be implemented efficien...

AI Technical Summary

Problems solved by technology

Compared with the traditional level set method, this method has a certain improvement in the segmentation of dental CT sequences, but the calculation speed is too slow

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